Georgia (Ning-Yuan) Liu

Georgia (Ning-Yuan) Liu

she/her

Publications

You can also find my articles on my Google Scholar profile.

Measuring Similarity in Causal Graphs: A Framework for Semantic and Structural Analysis

Published in arXiv preprint arXiv:2503.11046, 2025

I evaluated existing NLP and graph kernel methods for comparing causal graphs—considering both structural differences and the meaning of variable names. Using AI-generated synthetic data, we simulated how different people might map the same system. Our findings highlight key trade-offs between structural and semantic metrics, paving the way for better tools to interpret both human- and AI-generated models.

Recommended citation: Liu, N. Y. G., Yang, F., & Jalali, M. S. (2025). Measuring Similarity in Causal Graphs: A Framework for Semantic and Structural Analysis. arXiv preprint arXiv:2503.11046
Link to Paper

The Relationships among Workload, Automation Reliance, and Human Errors in Safety-Critical Monitoring Roles

Published in Safety Science, 2024

Using real-world data from Railway Traffic Control Centers, I studied how workload and automation interact to impact human errors. We found that errors are minimized when tasks are either mostly manual or mostly automated—highlighting the risks of mid-level automation. This work offers actionable insights for managing safety in complex socio-technical systems.

Recommended citation: Liu, N. G., Triantis, K., & Roets, B. (2024). The Relationships among Workload, Automation Reliance, and Human Errors in Safety-Critical Monitoring Roles. Safety Science, 106775. https://doi.org/10.1016/j.ssci.2024.106775
Link to Paper

Workload Dynamics in Safety-Critical Monitoring Roles: Evidence from the Belgian Railway Network

Published in SSRN Electronic Journal, 2024

I developed a dynamic simulation model to understand how workload impacts mental fatigue in railway traffic controllers. Using real-world data from the Belgian railway system, the model identifies a “comfort zone” for workload and shows how fatigue builds up when operators are over- or underloaded. These insights can inform scheduling, policy, and safety planning in automated transportation environments.

Recommended citation: Liu, Ning-Yuan Georgia and Triantis, Konstantinos and Roets, Bart, Workload Dynamics in Safety-Critical Monitoring Roles: Evidence from the Belgian Railway Network. Available at SSRN: https://ssrn.com/abstract=4798034 or http://dx.doi.org/10.2139/ssrn.4798034
Link to Paper

A multi-dimensional index of evaluating systems thinking skills from textual data

Published in Systems Research and Behavioral Science, 2024

I developed a quantitative framework to assess systems thinking (ST) skills from text, addressing the challenge of evaluating ST’s multi-dimensional nature. Using student data, we built an integrated ST index combining statistical and optimization methods (PCA, DEA, bootstrapping). The model helps identify how factors like math background and international experience relate to ST skill development.

Recommended citation: Liu, N. G., Mahmoudi, H., Triantis, K., & Ghaffarzadegan, N. (2024). A multi‐dimensional index of evaluating systems thinking skills from textual data. Systems Research and Behavioral Science. https://doi.org/10.1002/sres.3033
Link to Paper

Leveraging Large Language Models for Automated Causal Loop Diagram Generation: Enhancing System Dynamics Modeling through Curated Prompting Techniques

Published in SSRN Electronic Journal, 2024

I developed and tested a prompting strategies for translating dynamic hypotheses (text) into causal loop diagrams (CLDs) using large language models (LLMs). With curated prompting, LLMs can generate CLDs comparable in quality to expert-built ones—dramatically speeding up the modeling process and lowering the barrier for novice system dynamicists.

Recommended citation: Liu, N. G., keith, D. (2024). Leveraging Large Language Models for Automated Causal Loop Diagram Generation: Enhancing System Dynamics Modeling through Curated Prompting Techniques. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4906094
Link to Paper